from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	NaN	6.207	0.0	-1	1	NaN	0.053	0.004	NaN	0.245	0.246	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	NaN	6.176	0.0	-1	5	NaN	0.054	0.002	NaN	0.239	0.239	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	NaN	6.302	0.0	1	100	NaN	0.051	0.002	NaN	0.249	0.249	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.000	NaN	5.535	0.0	-1	100	NaN	0.056	0.002	NaN	0.259	0.259	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	NaN	6.126	0.0	1	5	NaN	0.055	0.001	NaN	0.239	0.239	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	NaN	5.974	0.0	1	1	NaN	0.057	0.005	NaN	0.233	0.234	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.334	0.0	-1	1	NaN	0.009	0.000	NaN	0.545	0.546	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.344	0.0	-1	5	NaN	0.009	0.000	NaN	0.527	0.528	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.312	0.0	1	100	NaN	0.009	0.000	NaN	0.570	0.570	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.327	0.0	-1	100	NaN	0.009	0.000	NaN	0.524	0.524	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.319	0.0	1	5	NaN	0.009	0.001	NaN	0.550	0.551	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.340	0.0	1	1	NaN	0.009	0.000	NaN	0.524	0.524	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.059	0.120	NaN	0.000	0.002	-1	1	0.663	0.404	0.012	0.687	5.097	5.099	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.004	NaN	0.000	0.027	-1	1	1.000	0.011	0.000	1.000	2.475	2.475	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.870	0.062	NaN	0.000	0.003	-1	5	0.757	0.401	0.009	0.742	7.149	7.151	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.003	NaN	0.000	0.027	-1	5	1.000	0.011	0.000	1.000	2.521	2.522	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.235	0.026	NaN	0.000	0.002	1	100	0.882	0.450	0.005	0.875	4.971	4.971	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.001	NaN	0.000	0.025	1	100	1.000	0.011	0.000	0.000	2.300	2.300	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.878	0.070	NaN	0.000	0.003	-1	100	0.882	0.457	0.019	0.875	6.297	6.302	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.005	NaN	0.000	0.028	-1	100	1.000	0.012	0.001	0.000	2.424	2.443	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.242	0.020	NaN	0.000	0.002	1	5	0.757	0.409	0.014	0.742	5.489	5.492	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	NaN	0.000	0.025	1	5	1.000	0.011	0.000	1.000	2.302	2.304	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.416	0.018	NaN	0.001	0.001	1	1	0.663	0.406	0.011	0.687	3.483	3.484	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	1	1.000	0.011	0.000	1.000	2.005	2.007	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.717	0.042	NaN	0.000	0.002	-1	1	0.896	0.084	0.002	0.967	20.487	20.495	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	NaN	0.000	0.004	-1	1	1.000	0.001	0.000	1.000	7.598	7.617	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.651	0.050	NaN	0.000	0.003	-1	5	0.922	0.087	0.003	0.974	30.421	30.444	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.003	NaN	0.000	0.008	-1	5	1.000	0.001	0.000	1.000	11.895	12.362	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.137	0.035	NaN	0.000	0.002	1	100	0.929	0.141	0.005	0.975	15.202	15.214	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	NaN	0.000	0.004	1	100	1.000	0.001	0.000	1.000	5.179	5.374	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.627	0.026	NaN	0.000	0.003	-1	100	0.929	0.138	0.005	0.975	19.044	19.055	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	1.000	9.596	9.626	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.057	0.021	NaN	0.000	0.002	1	5	0.922	0.088	0.004	0.974	23.376	23.395	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	1.000	5.245	5.262	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.175	0.018	NaN	0.000	0.001	1	1	0.896	0.084	0.003	0.967	13.910	13.920	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	1.000	3.692	3.698	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.917	0.026	NaN	0.027	0.0	-1	1	NaN	0.789	0.027	NaN	3.698	3.700	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.631	0.071	NaN	0.017	0.0	-1	5	NaN	0.773	0.009	NaN	5.993	5.994	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.359	0.060	NaN	0.018	0.0	1	100	NaN	0.737	0.013	NaN	5.911	5.912	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.757	0.158	NaN	0.017	0.0	-1	100	NaN	0.761	0.013	NaN	6.252	6.253	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.356	0.145	NaN	0.018	0.0	1	5	NaN	0.726	0.018	NaN	5.998	6.000	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.622	0.054	NaN	0.017	0.0	1	1	NaN	0.762	0.011	NaN	6.063	6.063	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.018	0.0	-1	1	NaN	0.004	0.002	NaN	0.227	0.258	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.024	0.0	-1	5	NaN	0.001	0.001	NaN	0.510	0.613	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.023	0.0	1	100	NaN	0.001	0.001	NaN	0.637	0.709	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.025	0.0	-1	100	NaN	0.001	0.000	NaN	0.643	0.648	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.024	0.0	1	5	NaN	0.001	0.000	NaN	0.671	0.673	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.025	0.0	1	1	NaN	0.001	0.000	NaN	0.659	0.662	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.851	1.033	NaN	0.000	0.001	-1	1	0.929	0.128	0.004	0.910	6.649	6.652	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	1.000	10.879	11.267	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.094	0.351	NaN	0.000	0.001	-1	5	0.946	0.230	0.006	0.941	4.761	4.763	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	1.000	7.410	7.647	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.830	0.494	NaN	0.000	0.006	1	100	0.951	0.694	0.018	0.940	8.397	8.400	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	1.000	4.054	4.182	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.376	0.283	NaN	0.000	0.003	-1	100	0.951	0.691	0.016	0.940	4.886	4.887	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	1.000	6.679	6.913	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.880	0.392	NaN	0.000	0.002	1	5	0.946	0.232	0.009	0.941	8.121	8.126	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	0.000	0.000	1.000	4.297	4.413	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.031	0.288	NaN	0.000	0.001	1	1	0.929	0.126	0.004	0.910	8.187	8.191	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	3.879	4.038	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.016	NaN	0.000	0.000	-1	1	0.891	0.001	0.000	0.879	67.911	68.080	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.000	0.000	1.000	17.792	17.993	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.034	0.003	NaN	0.000	0.000	-1	5	0.911	0.001	0.000	0.905	43.130	43.159	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	1.000	21.435	21.722	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.045	0.006	NaN	0.000	0.000	1	100	0.894	0.006	0.001	0.917	8.101	8.136	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	1.000	5.408	5.478	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.048	0.006	NaN	0.000	0.000	-1	100	0.894	0.006	0.002	0.917	7.319	7.538	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	100	1.000	0.000	0.000	1.000	12.754	15.943	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.000	NaN	0.001	0.000	1	5	0.911	0.001	0.000	0.905	36.983	36.999	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	1.000	5.647	5.743	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.028	0.001	NaN	0.001	0.000	1	1	0.891	0.001	0.000	0.879	46.360	47.902	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	6.256	6.348	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.619	0.193	30	0.026	0.0	random	NaN	0.302	0.007	NaN	2.051	2.052	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.652	0.079	30	0.025	0.0	k-means++	NaN	0.329	0.006	NaN	1.979	1.979	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.727	0.124	30	0.119	0.0	random	NaN	3.780	0.049	NaN	1.780	1.780	See	See
9	KMeans_tall	fit	1000000	1000000	100	7.153	0.016	30	0.112	0.0	k-means++	NaN	4.002	0.033	NaN	1.787	1.787	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	random	0.001	0.0	0.0	0.001	7.782	10.403	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	1.000	10.315	10.437	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	k-means++	0.001	0.0	0.0	0.001	7.943	8.668	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	1.000	11.668	11.813	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.395	0.000	random	0.002	0.0	0.0	0.002	4.873	6.862	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	1.000	10.449	10.601	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.394	0.000	k-means++	0.002	0.0	0.0	0.001	7.288	7.556	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	1.000	11.377	11.516	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.094	0.002	20	0.002	0.0	random	NaN	0.050	0.003	NaN	1.890	1.894	See	See
3	KMeans_short	fit	10000	10000	2	0.296	0.006	20	0.001	0.0	k-means++	NaN	0.128	0.004	NaN	2.317	2.318	See	See
6	KMeans_short	fit	10000	10000	100	0.318	0.005	20	0.025	0.0	random	NaN	0.247	0.003	NaN	1.290	1.290	See	See
9	KMeans_short	fit	10000	10000	100	1.080	0.024	20	0.007	0.0	k-means++	NaN	0.591	0.006	NaN	1.825	1.825	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.008	0.000	random	0.000	0.001	0.0	-0.001	3.824	3.831	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	1.000	0.000	0.0	1.000	8.263	9.882	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	0.001	0.0	-0.001	3.692	3.710	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	1.000	0.000	0.0	1.000	10.057	10.195	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.235	0.000	random	0.279	0.001	0.0	0.294	2.283	2.300	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	1.000	0.000	0.0	1.000	8.569	8.684	See	See
10	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.224	0.000	k-means++	0.317	0.001	0.0	0.257	2.442	2.456	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	1.000	0.000	0.0	1.000	9.312	9.491	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	15.972	0.564	[20]	0.050	0.000	NaN	NaN	NaN	NaN	NaN	2.91	0.048	NaN	5.489	5.490	See	See
3	LogisticRegression	fit	1000	1000	10000	1.419	0.700	[26]	0.056	0.001	NaN	NaN	NaN	NaN	NaN	1.12	0.032	NaN	1.267	1.267	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.000	[20]	1.560	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.55	0.625	1.233	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.010	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	1.00	0.360	0.362	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	3.452	0.0	NaN	NaN	NaN	NaN	0.35	0.008	0.001	0.28	0.296	0.297	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.001	[26]	0.183	0.0	NaN	NaN	NaN	NaN	0.00	0.002	0.000	0.00	0.248	0.248	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.294	0.006	NaN	0.272	0.0	NaN	NaN	NaN	0.304	0.006	NaN	0.967	0.967	See	See
3	Ridge	fit	1000000	1000000	100	1.277	0.068	NaN	0.627	0.0	NaN	NaN	NaN	0.428	0.263	NaN	2.984	3.502	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	NaN	6.662	0.0	NaN	NaN	0.083	0.02	0.001	0.122	0.594	0.594	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	NaN	0.900	0.0	NaN	NaN	NaN	0.00	0.000	NaN	0.600	0.612	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	NaN	4.919	0.0	NaN	NaN	1.000	0.00	0.000	1.000	0.451	0.637	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	NaN	0.009	0.0	NaN	NaN	NaN	0.00	0.000	NaN	0.715	0.738	See	See